Interactive intelligent troubleshooting guiding method, system, equipment and medium
Through interactive intelligent fault detection guidance methods and intelligent fault detection recommendation engine, independent optimization fault knowledge base, fault detection guidance model and strategies in IETM, the traditional fault detection efficiency and accuracy problems are solved, and efficient and accurate troubleshooting is achieved.
Patent Information
- Application Number
- CN202411841365.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-13
- Publication Date
- 2025-03-28
AI Technical Summary
Traditional aircraft installation equipment troubleshooting depends on personal experience and lagging technical data, which makes it difficult to ensure the efficiency and accuracy of failure, especially under the complex structure and intelligent system of the new generation of installation equipment.
Using an interactive intelligent fault detection guidance method, we provide comprehensive fault detection guidance strategies to improve fault detection efficiency and accuracy through the intelligent fault detection recommendation engine.
The independent optimal fault detection strategy has been realized, the fault detection efficiency and accuracy have been improved, and the intelligent and interactive fault detection guidance capabilities have been enhanced.
Smart Images
Figure CN119719301A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing technology, and in particular to an interactive intelligent troubleshooting guidance method, system, device and medium. Background Art
[0002] Traditional troubleshooting of aircraft equipment, engines and other equipment mainly relies on the personal experience and technical data of operators. On the one hand, personal experience varies greatly, and the troubleshooting efficiency and success rate cannot be accurately evaluated; on the other hand, traditional technical data are mainly paper manuals or electronic text manuals, and the troubleshooting strategy updates are relatively lagging, and the troubleshooting information query efficiency and accuracy are low. The new generation of equipment gradually adopts complex body structures, sophisticated sensors, and integrated and intelligent electronic systems. The fault complexity is high, and it is difficult to guarantee the troubleshooting efficiency and accuracy by relying solely on traditional troubleshooting methods. Summary of the invention
[0003] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides an interactive intelligent troubleshooting guidance method, system, device and medium. Through an intelligent troubleshooting recommendation engine, it independently optimizes and recommends the fault knowledge base, troubleshooting guidance model and troubleshooting strategies in IETM, and adopts interactive and intelligent troubleshooting guidance means to improve troubleshooting efficiency and accuracy.
[0004] In order to achieve the above object, the main technical solutions adopted by the present invention include: An interactive intelligent troubleshooting guidance method comprises the following steps: Collect equipment fault record data, perform structured processing, extract troubleshooting knowledge, perform knowledge fusion and disambiguation, and store it in the fault knowledge base; Provides visual interactive modeling tools to build fault trees, edit troubleshooting guidance strategies for fault events, and automatically generate interactive troubleshooting guidance models; Build an intelligent troubleshooting recommendation engine, access fault data, and automatically recommend the best troubleshooting strategy from fault knowledge, interactive electronic technical manual IETM, and troubleshooting guidance model; Collect user troubleshooting strategy usage behavior data and troubleshooting results, and automatically optimize the fault knowledge base, troubleshooting guidance model and intelligent troubleshooting recommendation engine.
[0005] Furthermore, the equipment fault record data is collected, structured, processed, troubleshooting knowledge is extracted, knowledge is integrated and eliminated, and stored in a fault knowledge base, specifically including: Collect equipment fault record data, including fault phenomenon, fault cause, occurrence time, fault part information, equipment model, equipment number, occurrence date, troubleshooting time, troubleshooting method, precautions, and support resource information; The fault record data is structured by constructing a fault professional word library, a general word library, and a stop word library, and adopting Chinese word segmentation technology to structure the fault record data; Extracting troubleshooting knowledge: The method for extracting troubleshooting knowledge refers to automatically extracting fault knowledge entities, attributes and relationship information from structured fault record data by combining machine learning with rules and vocabulary; Perform knowledge fusion and disambiguation. The knowledge fusion and disambiguation method refers to performing entity alignment and attribute alignment on the troubleshooting knowledge entities, attributes, and relationship information extracted from multiple fault record information using text similarity and semantic similarity algorithms, distinguishing inconsistent entity-attribute relationships, and fusing consistent entity-attribute relationships; The fault knowledge base is stored in a fault knowledge base, which is a relational database that stores fault knowledge entities, attributes, and relationships.
[0006] Furthermore, a visual interactive modeling tool is provided to build a fault tree, edit the troubleshooting guidance strategy of the fault event, and automatically generate an interactive troubleshooting guidance model, including: The visual interactive modeling tool is to graphically, modularly and window-basedly design the elements of the fault tree, provide graphic guidance strategies, and support users to complete the construction of the fault tree by dragging and clicking the mouse; The fault troubleshooting guidance strategy for editing fault events is to edit the fault location or troubleshooting methods of each level of fault events in the fault tree, and the methods include automatic troubleshooting methods based on data in the system and interactive troubleshooting guidance methods; The automatic troubleshooting method based on system data is to call the fault diagnosis model or directly connect to the system data to troubleshoot the fault event; the interactive troubleshooting guidance method is to interactively guide the user to complete the troubleshooting of the fault event by entering the name of the fault event, fault phenomenon description, fault location method, fault handling and verification method / repair suggestion, troubleshooting required support resources, fault rate and troubleshooting question and answer selection box; The automatically generated interactive troubleshooting guidance model is a backend program that automatically identifies various graphical components of the fault tree, extracts the entities, attributes, and relationship information of the fault tree, as well as the troubleshooting strategy information entered in the event box, calls the troubleshooting guidance optimization algorithm, calculates the optimal troubleshooting path, generates an interactive troubleshooting guidance model, and stores it in a database.
[0007] Furthermore, the intelligent troubleshooting recommendation engine is constructed to receive fault data and automatically recommend the optimal troubleshooting strategy from types such as fault knowledge, IETM, and troubleshooting guidance models, specifically including: The intelligent troubleshooting recommendation engine is an intelligent troubleshooting recommendation engine based on the fusion of term frequency-inverse document frequency TF-IDF and cosine similarity theory, which balances the weight of successful troubleshooting records and newly added troubleshooting knowledge / models / manuals, and recommends the optimal troubleshooting strategy; The lead fault data is the lead user input or other system / module fault information; The automatic recommendation of the optimal troubleshooting strategy from the types of fault knowledge, IETM, troubleshooting guidance model, etc. is to call the intelligent troubleshooting recommendation engine, recommend relevant troubleshooting knowledge, troubleshooting guidance model and troubleshooting cards in IETM according to the fault information, and sort them according to the calculated weights.
[0008] Furthermore, the collection of user troubleshooting strategy usage behavior data and troubleshooting results, and automatic optimization of the fault knowledge base, troubleshooting guidance model and intelligent troubleshooting recommendation engine specifically include: The collection of user troubleshooting strategy usage behavior data and troubleshooting results is to extract the user's key operation records, record the final troubleshooting results and the user's usage evaluation information during the user's use of the interactive intelligent troubleshooting guidance system, train and optimize the intelligent troubleshooting recommendation engine, train and optimize the guidance strategy of the troubleshooting guidance model, and train and optimize the fusion and disambiguation strategy of the fault knowledge base.
[0009] An interactive troubleshooting guidance system includes the following modules: The fault knowledge base management module is used to query, manually correct, merge and delete fault knowledge; The fault word library management module is used to query, add, delete and modify the keywords, new words, special words and stop words related to fault knowledge; IETM management module, used for version management operations such as importing, exporting, querying, enabling, disabling, and deleting IETM; The troubleshooting guidance model management module is used to query, modify, delete, enable, and disable the troubleshooting guidance model, as well as to perform visual modeling of the troubleshooting guidance model; The rapid troubleshooting support module is used to access fault data, call the intelligent troubleshooting recommendation engine, recommend the optimal troubleshooting guidance strategy, interactively guide users to complete troubleshooting tasks, collect user usage behavior records, troubleshooting results and user evaluation information, and independently optimize the fault knowledge base, troubleshooting guidance model and intelligent troubleshooting recommendation engine.
[0010] An electronic device, comprising: at least one memory; at least one processor; at least one program; The program is stored in the memory, and the processor executes at least one of the programs to implement the interactive intelligent troubleshooting guidance method.
[0011] A computer-readable storage medium stores a computer-executable signal, wherein the computer-executable signal is used to execute the interactive intelligent troubleshooting guidance method.
[0012] The beneficial effects of the present invention are: An interactive intelligent troubleshooting guidance method provided by the present invention provides a comprehensive troubleshooting guidance strategy, which can integrate a fault knowledge base, an interactive troubleshooting guidance model and an IETM troubleshooting strategy, realize autonomous optimization, and provide the best troubleshooting strategy recommendation.
[0013] The interactive troubleshooting guidance model described in the present invention can realize autonomous guidance of users to troubleshoot according to the optimal troubleshooting path; provide an interactive question-and-answer window, where users can complete troubleshooting with the system by entering information or selecting answers; and provide an automatic troubleshooting method based on a data model, which can automatically connect to relevant data in the database to complete fault location. Based on the above method, intelligent interactive troubleshooting guidance can be realized, improving the efficiency and accuracy of troubleshooting guidance.
[0014] The present invention makes full use of information and intelligent technical means to analyze and utilize the troubleshooting records of historical faults. On the one hand, it uses knowledge extraction, fusion, and disambiguation technologies to achieve automatic and intelligent accumulation of troubleshooting knowledge; on the other hand, it provides a visual fault tree modeling tool to assist users in completing fault analysis and generate an interactive troubleshooting guidance model with one click, which is another means of accumulating troubleshooting experience; in addition, based on the GJB6600 and S1000D specifications, a standard IETM management interface is developed to support users in querying IETM information.
[0015] The present invention provides an intelligent troubleshooting recommendation engine that can automatically recommend the optimal troubleshooting strategy based on fault keyword information. The intelligent troubleshooting recommendation engine is based on a method that integrates TF-IDF and cosine similarity theory to achieve autonomous optimization of troubleshooting knowledge, troubleshooting guidance model and IETM troubleshooting information, and can autonomously train and optimize the intelligent troubleshooting recommendation engine, fault knowledge base and interactive intelligent troubleshooting guidance model based on the user's key operation records, troubleshooting results and user evaluation information. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 This is a flowchart of the steps of an intelligent interactive troubleshooting guidance method according to an embodiment of the present invention; Figure 2 It is a structural block diagram of a time series interpretation system of flight parameter data according to an embodiment of the present invention; Figure 3 It is a schematic diagram of the steps of the fault knowledge base construction process of a specific embodiment of the present invention; Figure 4 It is a schematic diagram of the steps of the visual modeling process of the interactive troubleshooting guidance model according to a specific embodiment of the present invention; Figure 5 It is a schematic diagram of the steps of the interactive intelligent troubleshooting guide usage process of a specific embodiment of the present invention; Figure 6 It is a schematic diagram of the structure of an electronic device provided by a specific embodiment of the present invention. DETAILED DESCRIPTION
[0017] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only provided for the convenience of explanation and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.
[0018] Reference Figure 1 The present invention provides an interactive intelligent troubleshooting guidance method, which comprises the following steps: S1. Collect equipment fault record data, perform structured processing, extract troubleshooting knowledge, perform knowledge fusion and disambiguation, and store it in the fault knowledge base.
[0019] Specifically, refer to Figure 3 ,The fault knowledge base construction process includes the following steps: S11. Read newly added fault data.
[0020] Specifically, newly added fault data refers to fault record information registered by the user, which has completed troubleshooting and undergone closed-loop approval and confirmation, including fault phenomenon, fault cause, time of occurrence, faulty part information, equipment model, equipment number, date of occurrence, troubleshooting time, troubleshooting methods, precautions, and support resource information.
[0021] The faulty component information includes the faulty component name, component number, serial number, installation location, and installation date; The support resource information includes the tools, equipment, aviation materials, and support equipment information required for troubleshooting.
[0022] S12. Extract unstructured fault information.
[0023] Specifically, unstructured fault information refers to descriptive text information such as fault phenomenon, fault cause, troubleshooting method, precautions, etc. in fault data. These text information vary greatly depending on the individuality of the person entering the information.
[0024] S13. Call the Chinese word segmentation model to segment the fault information.
[0025] Specifically, the present invention utilizes Chinese word segmentation technology, combined with keywords, special words and other word libraries, to achieve structuring and standardization of descriptive text information.
[0026] S14, update the new word database.
[0027] Specifically, during the process of structuring fault information, new words discovered by the Chinese word segmentation model are stored in a new word database, and users can confirm the new words in the new word database.
[0028] S15. Extract fault knowledge and build entity, attribute and relationship models Specifically, it adopts a combination of machine learning, rules and vocabulary to automatically extract fault knowledge entities, attributes and relationship models from structured fault data to realize the modeling processing of fault knowledge.
[0029] S16. Read relevant knowledge from the fault knowledge base and perform knowledge fusion and disambiguation.
[0030] Specifically, knowledge fusion and disambiguation are to extract troubleshooting knowledge entities, attributes, and relationship information from multiple fault information, use text similarity, semantic similarity and other algorithms to perform entity alignment and attribute alignment, distinguish inconsistent entity-attribute relationships, and fuse consistent entity-attribute relationships.
[0031] S17. Storing the newly added fault knowledge into the fault knowledge base.
[0032] Specifically, the fault knowledge base is a relational database that can store entities, attributes, and relationship models in various types of fault knowledge.
[0033] S2. Use visual interactive modeling tools to build a fault tree, edit the troubleshooting guidance strategy of the fault event, automatically generate an interactive troubleshooting guidance model, and store it in the database.
[0034] Specifically, refer to Figure 4 , interactive troubleshooting guides the model to visualize the modeling process, which includes the following steps: S21. Configure the attribute information of the interactive troubleshooting guidance model, including the applicable scope, name, type, etc.; S22. Select the fault top event legend, drag it to the modeling area, double-click to enter the pop-up window, and configure the top event name and phenomenon description information; S23. According to the fault occurrence mechanism, select the fault intermediate event, bottom event, logic gate and other legends, drag them to the modeling area, connect them, and build a fault tree; S24. If there is an intermediate event, double-click the intermediate event legend and configure the intermediate event name and fault phenomenon description information in the pop-up window.
[0035] S25. Select the bottom event legends in turn and double-click to enter the bottom event information configuration pop-up window.
[0036] S26. According to the fault occurrence mechanism, if the bottom event can be directly located through the data in the database, select troubleshooting based on the data model in the pop-up window; S27. In the pop-up window, configure the name of the bottom event, location rule description, fault handling and verification / repair suggestions, failure rate, and required resources for troubleshooting in sequence; S28. Click the associated data model and select the data model required for fault location in the pop-up window. S29. According to the fault occurrence mechanism, if the bottom event requires an interactive guidance method to locate the fault, select troubleshooting based on interactive guidance in the pop-up window; S210. In the pop-up window, configure the name of the bottom event, troubleshooting method description, fault handling and verification / repair suggestions, failure rate, and required troubleshooting support resources in sequence; S211. If a question-and-answer guidance is required, click the question-and-answer guidance, and enter the required information, options, and answers for fault location in the pop-up window; S212. After all event information is configured, according to the failure rate data of each bottom event, the quantitative analysis model based on the minimum cut set is called to split the troubleshooting path and calculate the weight of each troubleshooting path.
[0037] S3. Connect fault data, call the intelligent troubleshooting recommendation engine, and automatically recommend the best troubleshooting strategy from fault knowledge, IETM (interactive electronic technical manual), troubleshooting guidance model and other types. Specifically, refer to Figure 5 , interactive intelligent troubleshooting guide usage process, the process includes the following related steps: S31. Read fault information.
[0038] Specifically, the newly added fault data may be a fault keyword entered by a user, or may be linked to a fault diagnosis result of a related fault diagnosis system / module or a fault name entered by a fault entry system / module.
[0039] S32. Call the intelligent troubleshooting recommendation engine to calculate the weights of the fault information-related fault knowledge, the interactive troubleshooting guidance model, and the IETM troubleshooting strategy, and sort them according to the scores.
[0040] S33. If a troubleshooting knowledge is selected, the troubleshooting knowledge guidance page is entered, which displays the fault phenomenon, fault component information, fault cause, fault handling and verification method, related historical fault case information and fault knowledge graph.
[0041] S34. If an interactive troubleshooting guidance strategy is selected, the interactive troubleshooting guidance page is entered, and the user is guided to troubleshoot according to the optimal troubleshooting path by using automatic calling of data model positioning, window guidance, and question and answer methods.
[0042] S35. If an IETM troubleshooting strategy is selected, enter the corresponding IETM work card page and perform troubleshooting according to the IETM troubleshooting process.
[0043] S4. Collect user usage behavior data and troubleshooting results, and automatically optimize the fault knowledge base, troubleshooting guidance model and intelligent troubleshooting recommendation engine.
[0044] Specifically, refer to Figure 5 , interactive intelligent troubleshooting guide usage process, the process includes the following related steps: S41. Record key operation behaviors during user use; S42. After the user completes troubleshooting, the fault knowledge, troubleshooting guide path or IETM number information used by the user to successfully troubleshoot is recorded; S43, recording the user's rating of the used troubleshooting strategy; S44. If the interactive troubleshooting guidance model is used, the troubleshooting path optimization algorithm is called according to the user score and the troubleshooting path of successful troubleshooting, and the weight of each troubleshooting path is recalculated; S45. If fault knowledge is used for troubleshooting, the fault knowledge optimization algorithm is called according to the user score and the successful troubleshooting result to recalculate the weight of the fault knowledge; S46. Based on the user ratings, user operation behavior records, and successful troubleshooting results, the intelligent troubleshooting recommendation engine optimization algorithm is called to recalculate various weight information in the recommendation algorithm.
[0045] Reference Figure 2 The present invention also provides an interactive intelligent troubleshooting guidance system, comprising: Fault knowledge base management module, used to query, manually correct, merge, delete and other management operations on fault knowledge; The fault word library management module is used to query, add, delete, modify and manage keywords, new words, special words, stop words, etc. related to fault knowledge; IETM management module, used to perform version management operations such as importing, exporting, querying, enabling, disabling, and deleting IETM; The troubleshooting guidance model management module is used to query, modify, delete, enable, disable and other management operations on the intelligent interactive troubleshooting guidance model, as well as visual modeling of the intelligent interactive troubleshooting guidance model; The rapid troubleshooting support module is used to access fault data, call the intelligent troubleshooting recommendation engine, recommend the optimal troubleshooting guidance strategy, interactively guide users to complete troubleshooting tasks, collect user usage behavior records, troubleshooting results and user evaluation information, and independently optimize the fault knowledge base, troubleshooting guidance model and intelligent troubleshooting recommendation engine.
[0046] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.
[0047] Reference Figure 6 , Figure 6 The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is used to execute the above method when it is executed.
[0048] The processor and the memory may be connected via a bus or other means.
[0049] The memory is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs and non-transitory computer executable programs, such as the method described in the embodiment of the present application. The processor implements the above method by running the non-transitory software programs and instructions stored in the memory.
[0050] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store and execute the above method. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one storage device storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0051] The non-transitory software program and instructions required to implement the above method are stored in the memory, and when executed by one or more processors, the above method is executed.
[0052] The embodiment of the present application also provides a computer-readable storage medium storing computer-executable instructions, and the computer-executable instructions are used to execute the above method.
[0053] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed on multiple network units; some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.
[0054] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium), as known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as a computer-readable instruction data structure, a program module, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, storage device storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.
[0055] It should also be understood that the various implementations provided in the embodiments of the present application can be combined arbitrarily to achieve different technical effects.
[0056] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.
Claims
1. An interactive intelligent troubleshooting guidance method, characterized in that: The following steps are involved: Collect equipment fault record data, perform structured processing, extract troubleshooting knowledge, perform knowledge fusion and disambiguation, and store it in the fault knowledge base; Provides visual interactive modeling tools to build fault trees, edit troubleshooting guidance strategies for fault events, and automatically generate interactive troubleshooting guidance models; Build an intelligent troubleshooting recommendation engine, access fault data, and automatically recommend the best troubleshooting strategy from fault knowledge, interactive electronic technical manual IETM, and troubleshooting guidance model; Collect user troubleshooting strategy usage behavior data and troubleshooting results, and automatically optimize the fault knowledge base, troubleshooting guidance model and intelligent troubleshooting recommendation engine.
2. The interactive intelligent troubleshooting guidance method according to claim 1, characterized in that: The collecting of equipment fault record data, performing structured processing, extracting troubleshooting knowledge, performing knowledge fusion and disambiguation, and storing the data in a fault knowledge base specifically includes: Collect equipment fault record data, including fault phenomenon, fault cause, occurrence time, fault part information, equipment model, equipment number, occurrence date, troubleshooting time, troubleshooting method, precautions, and support resource information; The fault record data is structured by constructing a fault professional word library, a general word library, and a stop word library, and adopting Chinese word segmentation technology to structure the fault record data; Extracting troubleshooting knowledge: The method for extracting troubleshooting knowledge refers to automatically extracting fault knowledge entities, attributes and relationship information from structured fault record data by combining machine learning with rules and vocabulary; Perform knowledge fusion and disambiguation. The knowledge fusion and disambiguation method refers to performing entity alignment and attribute alignment on the troubleshooting knowledge entities, attributes, and relationship information extracted from multiple fault record information using text similarity and semantic similarity algorithms, distinguishing inconsistent entity-attribute relationships, and fusing consistent entity-attribute relationships; The fault knowledge base is stored in a fault knowledge base, which is a relational database that stores fault knowledge entities, attributes, and relationships.
3. The interactive intelligent troubleshooting guidance method according to claim 1, characterized in that: Provides visual interactive modeling tools to build fault trees, edit troubleshooting guidance strategies for fault events, and automatically generate interactive troubleshooting guidance models, including: The visual interactive modeling tool is to graphically, modularly and window-basedly design the elements of the fault tree, provide graphic guidance strategies, and support users to complete the construction of the fault tree by dragging and clicking the mouse; The fault troubleshooting guidance strategy for editing fault events is to edit the fault location or troubleshooting methods of each level of fault events in the fault tree, and the methods include automatic troubleshooting methods based on data in the system and interactive troubleshooting guidance methods; The automatic troubleshooting method based on system data is to call the fault diagnosis model or directly connect to the system data to troubleshoot the fault event; the interactive troubleshooting guidance method is to interactively guide the user to complete the troubleshooting of the fault event by entering the name of the fault event, fault phenomenon description, fault location method, fault handling and verification method / repair suggestion, troubleshooting required support resources, fault rate and troubleshooting question and answer selection box; The automatically generated interactive troubleshooting guidance model is a backend program that automatically identifies various graphical components of the fault tree, extracts the entities, attributes, and relationship information of the fault tree, as well as the troubleshooting strategy information entered in the event box, calls the troubleshooting guidance optimization algorithm, calculates the optimal troubleshooting path, generates an interactive troubleshooting guidance model, and stores it in a database.
4. The interactive intelligent troubleshooting guidance method according to claim 1, characterized in that: The intelligent troubleshooting recommendation engine is constructed to receive fault data and automatically recommend the optimal troubleshooting strategy from types such as fault knowledge, IETM, and troubleshooting guidance models, specifically including: The intelligent troubleshooting recommendation engine is an intelligent troubleshooting recommendation engine based on the fusion of term frequency-inverse document frequency TF-IDF and cosine similarity theory, which balances the weight of successful troubleshooting records and newly added troubleshooting knowledge / models / manuals, and recommends the optimal troubleshooting strategy; The lead fault data is the lead user input or other system / module fault information; The automatic recommendation of the optimal troubleshooting strategy from the types of fault knowledge, IETM, troubleshooting guidance model, etc. is to call the intelligent troubleshooting recommendation engine, recommend relevant troubleshooting knowledge, troubleshooting guidance model and troubleshooting cards in IETM according to the fault information, and sort them according to the calculated weights.
5. The interactive intelligent troubleshooting guidance method according to claim 1, characterized in that: The collection of user troubleshooting strategy usage behavior data and troubleshooting results, and automatic optimization of the fault knowledge base, troubleshooting guidance model, and intelligent troubleshooting recommendation engine specifically include: The collection of user troubleshooting strategy usage behavior data and troubleshooting results is to extract the user's key operation records, record the final troubleshooting results and the user's usage evaluation information during the user's use of the interactive intelligent troubleshooting guidance system, train and optimize the intelligent troubleshooting recommendation engine, train and optimize the guidance strategy of the troubleshooting guidance model, and train and optimize the fusion and disambiguation strategy of the fault knowledge base.
6. An interactive troubleshooting guidance system, characterized in that: Includes the following modules: The fault knowledge base management module is used to query, manually correct, merge and delete fault knowledge; The fault word library management module is used to query, add, delete and modify the keywords, new words, special words and stop words related to fault knowledge; IETM management module, used for version management operations such as importing, exporting, querying, enabling, disabling, and deleting IETM; The troubleshooting guidance model management module is used to query, modify, delete, enable, and disable the troubleshooting guidance model, as well as to perform visual modeling of the troubleshooting guidance model; The rapid troubleshooting support module is used to access fault data, call the intelligent troubleshooting recommendation engine, recommend the optimal troubleshooting guidance strategy, interactively guide users to complete troubleshooting tasks, collect user usage behavior records, troubleshooting results and user evaluation information, and independently optimize the fault knowledge base, troubleshooting guidance model and intelligent troubleshooting recommendation engine.
7. An electronic device comprising: at least one memory; at least one processor; at least one program; The program is stored in the memory, and the processor executes at least one of the programs to implement an interactive intelligent troubleshooting guidance method as described in any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer-executable signal, and the computer-executable signal is used to execute an interactive intelligent troubleshooting guidance method as described in any one of claims 1 to 5.